OpenRaiser/Intern-Atlas huggingface dataset:
MVHM: A Large-Scale Multi-View Hand Mesh Benchmark for Accurate 3D Hand Pose Estimation
Venue: arXiv cs.CV 2020
Year: 2020

Estimating 3D hand poses from a single RGB image is challenging because depth ambiguity leads the problem ill-posed. Training hand pose estimators with 3D hand mesh annotations and multi-view images often results in significant performance gains. However, existing multi-view datasets are relatively small with hand joints annotated by off-the-shelf trackers or automated through model predictions, both of which may be inaccurate and can introduce biases. Collecting a large-scale multi-view 3D hand pose images with accurate mesh and joint annotations is valuable but strenuous. In this paper, we design a spin match algorithm that enables a rigid mesh model matching with any target mesh ground truth. Based on the match algorithm, we propose an efficient pipeline to generate a large-scale multi-view hand mesh (MVHM) dataset with accurate 3D hand mesh and joint labels. We further present a multi-view hand pose estimation approach to verify that training a hand pose estimator with our generated dataset greatly enhances the performance. Experimental results show that our approach achieves the performance of 0.990 in $\text{AUC}_{\text{20-50}}$ on the MHP dataset compared to the previous state-of-the-art of 0.939 on this dataset. Our datasset is public available. \footnote{\url{https://github.com/Kuzphi/MVHM}} Our datasset is available at~\href{https://github.com/Kuzphi/MVHM}{\color{blue}{https://github.com/Kuzphi/MVHM}}.
Amortized Q-learning with Model-based Action Proposals for Autonomous Driving on Highways
Venue: arXiv cs.LG 2020
Year: 2020

Well-established optimization-based methods can guarantee an optimal trajectory for a short optimization horizon, typically no longer than a few seconds. As a result, choosing the optimal trajectory for this short horizon may still result in a sub-optimal long-term solution. At the same time, the resulting short-term trajectories allow for effective, comfortable and provable safe maneuvers in a dynamic traffic environment. In this work, we address the question of how to ensure an optimal long-term driving strategy, while keeping the benefits of classical trajectory planning. We introduce a Reinforcement Learning based approach that coupled with a trajectory planner, learns an optimal long-term decision-making strategy for driving on highways. By online generating locally optimal maneuvers as actions, we balance between the infinite low-level continuous action space, and the limited flexibility of a fixed number of predefined standard lane-change actions. We evaluated our method on realistic scenarios in the open-source traffic simulator SUMO and were able to achieve better performance than the 4 benchmark approaches we compared against, including a random action selecting agent, greedy agent, high-level, discrete actions agent and an IDM-based SUMO-controlled agent.
Forecasting fuel combustion-related CO$_2$ emissions by a novel continuous fractional nonlinear grey Bernoulli model with Grey Wolf Optimizer
Venue: arXiv cs.CE 2020
Year: 2020

Foresight of CO$_2$ emissions from fuel combustion is essential for policy-makers to identify ready targets for effective reduction plans and further to improve energy policies and plans. For the purpose of accurately forecasting the future development of China's CO$_2$ emissions from fuel combustion, a novel continuous fractional nonlinear grey Bernoulli model is developed in this paper. The fractional nonlinear grey Bernoulli model already in place is known that has a fixed first-order derivative that impairs the predictive performance to some extent. To address this problem, in the newly proposed model, a flexible variable is introduced into the order of derivative, freeing it from integer-order accumulation. In order to further improve the performance of the newly proposed model, a meta-heuristic algorithm, namely Grey Wolf Optimizer (GWO), is determined to the emerging coefficients. To demonstrate the effectiveness, two real examples and China's fuel combustion-related CO$_2$ emissions are used for model validation by comparing with other benchmark models, the results show the proposed model outperforms competitors. Thus, the future development trend of fuel combustion-related CO$_2$ emissions by 2023 are predicted, accounting for 10039.80 Million tons (Mt). In accordance with the forecasts, several suggestions are provided to curb carbon dioxide emissions.
Covert Communication in Intelligent Reflecting Surface-Assisted NOMA Systems: Design, Analysis, and Optimization
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we investigate covert communication in an intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) system, where a legitimate transmitter (Alice) applies NOMA for downlink and uplink transmissions with a covert user (Bob) and a public user (Roy) aided by an IRS. Specifically, we propose new IRS-assisted downlink and uplink NOMA schemes to hide the existence of Bob's covert transmission from a warden (Willie), which cost-effectively exploit the phase-shift uncertainty of the IRS and the non-orthogonal signal transmission of Roy as the cover medium without requiring additional uncertainty sources. Assuming the worst-case covert communication scenario where Willie can optimally adjust the detection threshold for his detector, we derive an analytical expression for the minimum average detection error probability of Willie achieved by each of the proposed schemes. To further enhance the covert communication performance, we propose to maximize the covert rates of Bob by jointly optimizing the transmit power and the IRS reflect beamforming, subject to given requirements on the covertness against Willie and the quality-of-service (QoS) at Roy. Simulation results demonstrate the covertness advantage of the proposed schemes and confirm the accuracy of the derived analytical results. Interestingly, it is found that covert communication is impossible without using IRS or NOMA for the considered setup while the proposed schemes can always guarantee positive covert rates.
On the Privacy and Integrity Risks of Contact-Tracing Applications
Venue: arXiv cs.CR 2020
Year: 2020

Smartphone-based contact-tracing applications are at the epicenter of the global fight against the Covid-19 pandemic. While governments and healthcare agencies are eager to mandate the deployment of such applications en-masse, they face increasing scrutiny from the popular press, security companies, and human rights watch agencies that fear the exploitation of these technologies as surveillance tools. Finding the optimal balance between community safety and privacy has been a challenge, and strategies to address these concerns have varied among countries. This paper describes two important attacks that affect a broad swath of contact-tracing applications. The first, referred to as contact-isolation attack, is a user-privacy attack that can be used to identify potentially infected patients in your neighborhood. The second is a contact-pollution attack that affects the integrity of contact tracing applications by causing them to produce a high volume of false-positive alerts. We developed prototype implementations and evaluated both attacks in the context of the DP-3T application framework, but these vulnerabilities affect a much broader class of applications. We found that both attacks are feasible and realizable with a minimal attacker work factor. We further conducted an impact assessment of these attacks by using a simulation study and measurements from the SafeGraph database. Our results indicate that attacks launched from a modest number (on the order of 10,000) of monitoring points can effectively decloak between 5-40\% of infected users in a major metropolis, such as Houston.
Deep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learning
Venue: arXiv cs.LG 2020
Year: 2020

In this article, the concepts of transfer and continual learning are introduced. The ensuing review reveals promising approaches for industrial deep transfer learning, utilizing methods of both classes of algorithms. In the field of computer vision, it is already state-of-the-art. In others, e.g. fault prediction, it is barely starting. However, over all fields, the abstract differentiation between continual and transfer learning is not benefitting their practical use. In contrast, both should be brought together to create robust learning algorithms fulfilling the industrial automation sector's requirements. To better describe these requirements, base use cases of industrial transfer learning are introduced.
Efficient Human Pose Estimation with Depthwise Separable Convolution and Person Centroid Guided Joint Grouping
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose efficient and effective methods for 2D human pose estimation. A new ResBlock is proposed based on depthwise separable convolution and is utilized instead of the original one in Hourglass network. It can be further enhanced by replacing the vanilla depthwise convolution with a mixed depthwise convolution. Based on it, we propose a bottom-up multi-person pose estimation method. A rooted tree is used to represent human pose by introducing person centroid as the root which connects to all body joints directly or hierarchically. Two branches of sub-networks are used to predict the centroids, body joints and their offsets to their parent nodes. Joints are grouped by tracing along their offsets to the closest centroids. Experimental results on the MPII human dataset and the LSP dataset show that both our single-person and multi-person pose estimation methods can achieve competitive accuracies with low computational costs.
Social Responsibility of Algorithms
Venue: arXiv cs.CY 2020
Year: 2020

Should we be concerned by the massive use of devices and algorithms which automatically handle an increasing number of everyday activities within our societies? The paper makes a short overview of the scientific investigation around this topic, showing that the development, existence and use of such autonomous artifacts is much older than the recent interest in machine learning monopolised artificial intelligence. We then categorise the impact of using such artifacts to the whole process of data collection, structuring, manipulation as well as in recommendation and decision making. The suggested framework allows to identify a number of challenges for the whole community of decision analysts, both researchers and practitioners.
EasyPBR: A Lightweight Physically-Based Renderer
Venue: arXiv cs.GR 2020
Year: 2020

Modern rendering libraries provide unprecedented realism, producing real-time photorealistic 3D graphics on commodity hardware. Visual fidelity, however, comes at the cost of increased complexity and difficulty of usage, with many rendering parameters requiring a deep understanding of the pipeline. We propose EasyPBR as an alternative rendering library that strikes a balance between ease-of-use and visual quality. EasyPBR consists of a deferred renderer that implements recent state-of-the-art approaches in physically based rendering. It offers an easy-to-use Python and C++ interface that allows high-quality images to be created in only a few lines of code or directly through a graphical user interface. The user can choose between fully controlling the rendering pipeline or letting EasyPBR automatically infer the best parameters based on the current scene composition. The EasyPBR library can help the community to more easily leverage the power of current GPUs to create realistic images. These can then be used as synthetic data for deep learning or for creating animations for academic purposes.
Source Separation and Depthwise Separable Convolutions for Computer Audition
Venue: arXiv cs.SD 2020
Year: 2020

Given recent advances in deep music source separation, we propose a feature representation method that combines source separation with a state-of-the-art representation learning technique that is suitably repurposed for computer audition (i.e. machine listening). We train a depthwise separable convolutional neural network on a challenging electronic dance music (EDM) data set and compare its performance to convolutional neural networks operating on both source separated and standard spectrograms. It is shown that source separation improves classification performance in a limited-data setting compared to the standard single spectrogram approach.
Unsupervised Regionalization of Particle-resolved Aerosol Mixing State Indices on the Global Scale
Venue: arXiv cs.LG 2020
Year: 2020

The aerosol mixing state significantly affects the climate and health impacts of atmospheric aerosol particles. Simplified aerosol mixing state assumptions, common in Earth System models, can introduce errors in the prediction of these aerosol impacts. The aerosol mixing state index, a metric to quantify aerosol mixing state, is a convenient measure for quantifying these errors. Global estimates of aerosol mixing state indices have recently become available via supervised learning models, but require regionalization to ease spatiotemporal analysis. Here we developed a simple but effective unsupervised learning approach to regionalize predictions of global aerosol mixing state indices. We used the monthly average of aerosol mixing state indices global distribution as the input data. Grid cells were then clustered into regions by the k-means algorithm without explicit spatial information as input. This approach resulted in eleven regions over the globe with specific spatial aggregation patterns. Each region exhibited a unique distribution of mixing state indices and aerosol compositions, showing the effectiveness of the unsupervised regionalization approach. This study defines "aerosol mixing state zones" that could be useful for atmospheric science research.
Brain Co-Processors: Using AI to Restore and Augment Brain Function
Venue: arXiv cs.AI 2020
Year: 2020

Brain-computer interfaces (BCIs) use decoding algorithms to control prosthetic devices based on brain signals for restoration of lost function. Computer-brain interfaces (CBIs), on the other hand, use encoding algorithms to transform external sensory signals into neural stimulation patterns for restoring sensation or providing sensory feedback for closed-loop prosthetic control. In this article, we introduce brain co-processors, devices that combine decoding and encoding in a unified framework using artificial intelligence (AI) to supplement or augment brain function. Brain co-processors can be used for a range of applications, from inducing Hebbian plasticity for rehabilitation after brain injury to reanimating paralyzed limbs and enhancing memory. A key challenge is simultaneous multi-channel neural decoding and encoding for optimization of external behavioral or task-related goals. We describe a new framework for developing brain co-processors based on artificial neural networks, deep learning and reinforcement learning. These "neural co-processors" allow joint optimization of cost functions with the nervous system to achieve desired behaviors. By coupling artificial neural networks with their biological counterparts, neural co-processors offer a new way of restoring and augmenting the brain, as well as a new scientific tool for brain research. We conclude by discussing the potential applications and ethical implications of brain co-processors.
On Infusing Reachability-Based Safety Assurance within Planning Frameworks for Human-Robot Vehicle Interactions
Venue: arXiv cs.RO 2020
Year: 2020

Action anticipation, intent prediction, and proactive behavior are all desirable characteristics for autonomous driving policies in interactive scenarios. Paramount, however, is ensuring safety on the road -- a key challenge in doing so is accounting for uncertainty in human driver actions without unduly impacting planner performance. This paper introduces a minimally-interventional safety controller operating within an autonomous vehicle control stack with the role of ensuring collision-free interaction with an externally controlled (e.g., human-driven) counterpart while respecting static obstacles such as a road boundary wall. We leverage reachability analysis to construct a real-time (100Hz) controller that serves the dual role of (i) tracking an input trajectory from a higher-level planning algorithm using model predictive control, and (ii) assuring safety by maintaining the availability of a collision-free escape maneuver as a persistent constraint regardless of whatever future actions the other car takes. A full-scale steer-by-wire platform is used to conduct traffic weaving experiments wherein two cars, initially side-by-side, must swap lanes in a limited amount of time and distance, emulating cars merging onto/off of a highway. We demonstrate that, with our control stack, the autonomous vehicle is able to avoid collision even when the other car defies the planner's expectations and takes dangerous actions, either carelessly or with the intent to collide, and otherwise deviates minimally from the planned trajectory to the extent required to maintain safety.
Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning
Venue: arXiv cs.CV 2020
Year: 2020

Recently, hyperspectral image (HSI) classification approaches based on deep learning (DL) models have been proposed and shown promising performance. However, because of very limited available training samples and massive model parameters, DL methods may suffer from overfitting. In this paper, we propose an end-to-end 3-D lightweight convolutional neural network (CNN) (abbreviated as 3-D-LWNet) for limited samples-based HSI classification. Compared with conventional 3-D-CNN models, the proposed 3-D-LWNet has a deeper network structure, less parameters, and lower computation cost, resulting in better classification performance. To further alleviate the small sample problem, we also propose two transfer learning strategies: 1) cross-sensor strategy, in which we pretrain a 3-D model in the source HSI data sets containing a greater number of labeled samples and then transfer it to the target HSI data sets and 2) cross-modal strategy, in which we pretrain a 3-D model in the 2-D RGB image data sets containing a large number of samples and then transfer it to the target HSI data sets. In contrast to previous approaches, we do not impose restrictions over the source data sets, in which they do not have to be collected by the same sensors as the target data sets. Experiments on three public HSI data sets captured by different sensors demonstrate that our model achieves competitive performance for HSI classification compared to several state-of-the-art methods
Attention-based Saliency Hashing for Ophthalmic Image Retrieval
Venue: arXiv cs.CV 2020
Year: 2020

Deep hashing methods have been proved to be effective for the large-scale medical image search assisting reference-based diagnosis for clinicians. However, when the salient region plays a maximal discriminative role in ophthalmic image, existing deep hashing methods do not fully exploit the learning ability of the deep network to capture the features of salient regions pointedly. The different grades or classes of ophthalmic images may be share similar overall performance but have subtle differences that can be differentiated by mining salient regions. To address this issue, we propose a novel end-to-end network, named Attention-based Saliency Hashing (ASH), for learning compact hash-code to represent ophthalmic images. ASH embeds a spatial-attention module to focus more on the representation of salient regions and highlights their essential role in differentiating ophthalmic images. Benefiting from the spatial-attention module, the information of salient regions can be mapped into the hash-code for similarity calculation. In the training stage, we input the image pairs to share the weights of the network, and a pairwise loss is designed to maximize the discriminability of the hash-code. In the retrieval stage, ASH obtains the hash-code by inputting an image with an end-to-end manner, then the hash-code is used to similarity calculation to return the most similar images. Extensive experiments on two different modalities of ophthalmic image datasets demonstrate that the proposed ASH can further improve the retrieval performance compared to the state-of-the-art deep hashing methods due to the huge contributions of the spatial-attention module.
An Empirical Survey of Unsupervised Text Representation Methods on Twitter Data
Venue: arXiv cs.CL 2020
Year: 2020

The field of NLP has seen unprecedented achievements in recent years. Most notably, with the advent of large-scale pre-trained Transformer-based language models, such as BERT, there has been a noticeable improvement in text representation. It is, however, unclear whether these improvements translate to noisy user-generated text, such as tweets. In this paper, we present an experimental survey of a wide range of well-known text representation techniques for the task of text clustering on noisy Twitter data. Our results indicate that the more advanced models do not necessarily work best on tweets and that more exploration in this area is needed.
A multi-agent evolutionary robotics framework to train spiking neural networks
Venue: arXiv cs.NE 2020
Year: 2020

A novel multi-agent evolutionary robotics (ER) based framework, inspired by competitive evolutionary environments in nature, is demonstrated for training Spiking Neural Networks (SNN). The weights of a population of SNNs along with morphological parameters of bots they control in the ER environment are treated as phenotypes. Rules of the framework select certain bots and their SNNs for reproduction and others for elimination based on their efficacy in capturing food in a competitive environment. While the bots and their SNNs are given no explicit reward to survive or reproduce via any loss function, these drives emerge implicitly as they evolve to hunt food and survive within these rules. Their efficiency in capturing food as a function of generations exhibit the evolutionary signature of punctuated equilibria. Two evolutionary inheritance algorithms on the phenotypes, Mutation and Crossover with Mutation, are demonstrated. Performances of these algorithms are compared using ensembles of 100 experiments for each algorithm. We find that Crossover with Mutation promotes 40% faster learning in the SNN than mere Mutation with a statistically significant margin.
Domain Generalization for Session-Independent Brain-Computer Interface
Venue: arXiv cs.HC 2020
Year: 2020

The inter/intra-subject variability of electroencephalography (EEG) makes the practical use of the brain-computer interface (BCI) difficult. In general, the BCI system requires a calibration procedure to acquire subject/session-specific data to tune the model every time the system is used. This problem is recognized as a major obstacle to BCI, and to overcome it, an approach based on domain generalization (DG) has recently emerged. The main purpose of this paper is to reconsider how the zero-calibration problem of BCI for a realistic situation can be overcome from the perspective of DG tasks. In terms of the realistic situation, we have focused on creating an EEG classification framework that can be applied directly in unseen sessions, using only multi-subject/-session data acquired previously. Therefore, in this paper, we tested four deep learning models and four DG algorithms through leave-one-session-out validation. Our experiment showed that deeper and larger models were effective in cross-session generalization performance. Furthermore, we found that none of the explicit DG algorithms outperformed empirical risk minimization. Finally, by comparing the results of fine-tuning using subject-specific data, we found that subject-specific data may deteriorate unseen session classification performance due to inter-session variability.